uda is a particular case of da, in which we rely only on supervision in the source domain without using the labels of the target data. For a more comprehensive survey about da see sec:back:domainadaptation.
Traditional uda approaches have been developed to address the problem of image classification, and they try to align features across the two domains ([1]}, [2]}). However, as pointed out in [3]}, they do not perform well in other tasks. More recent advances also involve the semantic segmentation task. In this case, adversarial training for uda is the most employed approach. It includes two networks. The first predicts the segmentation maps for the input source image. The second acts as a discriminator, taking the feature maps from the segmentation network and trying to predict the input domain. The adversarial loss, computed from the discriminator output, tries to make the distributions of the two domains more similar. The first to apply such a technique is [4]}. More recently, the work proposed in [5]} employs a residual network and adversarial training to make the source feature maps closer to the target ones. The authors of [6]} combine semantic segmentation and depth estimation to boost the adaptation performance, providing to the discriminator the segmentation and the depth prediction maps jointly. Another interesting work that inspired this paper is [7]}, where the authors applied adversarial training to the output space taking advantage of the structural consistency across domains.
